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  1. Article

    Open Access

    Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

    Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up observations. Ther...

    Hannah R. Kerner, Kiri L. Wagstaff, Brian D. Bue… in Data Mining and Knowledge Discovery (2020)

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  2. No Access

    Article

    Visualizing image content to explain novel image discovery

    The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends...

    Jake H. Lee, Kiri L. Wagstaff in Data Mining and Knowledge Discovery (2020)

  3. No Access

    Reference Work Entry In depth

    Constrained Clustering

    Kiri  L. Wagstaff in Encyclopedia of Machine Learning and Data Mining (2017)

  4. Article

    Machine learning for science and society

    The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perfor...

    Cynthia Rudin, Kiri L. Wagstaff in Machine Learning (2014)

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  5. Article

    Machine learning in space: extending our reach

    We introduce the challenge of using machine learning effectively in space applications and motivate the domain for future researchers. Machine learning can be used to enable greater autonomy to improve the dur...

    Amy McGovern, Kiri L. Wagstaff in Machine Learning (2011)

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  6. No Access

    Reference Work Entry In depth

    Constrained Clustering

    Kiri L. Wagstaff in Encyclopedia of Machine Learning (2010)

  7. No Access

    Article

    Progressive refinement for support vector machines

    Support vector machines (SVMs) have good accuracy and generalization properties, but they tend to be slow to classify new examples. In contrast to previous work that aims to reduce the time required to fully c...

    Kiri L. Wagstaff, Michael Kocurek, Dominic Mazzoni… in Data Mining and Knowledge Discovery (2010)

  8. No Access

    Chapter and Conference Paper

    Value, Cost, and Sharing: Open Issues in Constrained Clustering

    Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering method...

    Kiri L. Wagstaff in Knowledge Discovery in Inductive Databases (2007)

  9. Chapter and Conference Paper

    Active Learning with Irrelevant Examples

    Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrele...

    Dominic Mazzoni, Kiri L. Wagstaff, Michael C. Burl in Machine Learning: ECML 2006 (2006)

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  10. Chapter and Conference Paper

    Measuring Constraint-Set Utility for Partitional Clustering Algorithms

    Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves performance, with respe...

    Ian Davidson, Kiri L. Wagstaff, Sugato Basu in Knowledge Discovery in Databases: PKDD 2006 (2006)

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  11. No Access

    Chapter and Conference Paper

    Active Constrained Clustering by Examining Spectral Eigenvectors

    This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) deriv...

    Qianjun Xu, Marie desJardins, Kiri L. Wagstaff in Discovery Science (2005)